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Beautiful Data by Toby Segaran, Jeff Hammerbacher

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The Unreasonable Effectiveness of Data

In a recent paper, a trio of Google researchers distilled what they have learned from trying to solve some of machine learning's most difficult challenges. When discussing the problems of speech recognition and machine translation, they state that, "invariably, simple models and a lot of data trump more elaborate models based on less data." I don't intend to debate their findings; certainly there are domains where elaborate models are successful. Yet based on their experiences, there does exist a wide class of problems for which more data and simple models are better.

At Facebook, Hadoop was our tool for exploiting the unreasonable effectiveness of data. For example, when we were translating the site into other languages, we tried to target users who spoke a specific language to enlist their help in the translation task. One of our Data Scientists, Cameron Marlow, crawled all of Wikipedia and built character trigram frequency counts per language. Using these frequency counts, he built a simple classifier that could look at a set of wall posts authored by a user and determine his spoken language. Using this classifier, we were able to actively recruit users into our translation program in a targeted fashion. Both Facebook and Google use natural language data in many applications; see Chapter 14 of this book for Peter Norvig's exploration of the topic.

The observations from Google point to a third line of evolution for modern business intelligence ...

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